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770
The Fundamental matrix: theory, algorithms, and stability analysis
 International Journal of Computer Vision
, 1995
"... In this paper we analyze in some detail the geometry of a pair of cameras, i.e. a stereo rig. Contrarily to what has been done in the past and is still done currently, for example in stereo or motion analysis, we do not assume that the intrinsic parameters of the cameras are known (coordinates of th ..."
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Cited by 272 (13 self)
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for establishing correspondences between two pairs of images. This information is fundamentally projective and is hidden in a confusing manner in the commonly used formalism of the Essential matrix introduced by LonguetHiggins [40]. This paper clarifies the projective nature of the correspondence problem
Multidimensional Independent Component Analysis.
 In Proc. Int. Workshop on HigherOrder Stat
, 1998
"... This discussion paper proposes to generalize the notion of Independent Component Analysis (ICA) to the notion of Multidimensional Independent Component Analysis (MICA). We start from the ICA or blind source separation (BSS) model and show that it can be uniquely identified provided it is properly p ..."
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Cited by 257 (15 self)
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SOURCE SEPARATION We start by considering the blind source separation (BSS) problem in the simplest model: an n\Theta1 vector of observations x is modeled as x = As; with A = [a1 ; : : : ; an ] (1) where s is a n\Theta1 vector with statistically independent components and matrix A is an n \Theta n
Robust Linear Programming Discrimination Of Two Linearly Inseparable Sets
, 1992
"... INTRODUCTION We consider the two pointsets A and B in the ndimensional real space R n represented by the m \Theta n matrix A and the k \Theta n matrix B respectively. Our principal objective here is to formulate a single linear program with the following properties: (i) If the convex hulls of A ..."
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Cited by 239 (32 self)
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INTRODUCTION We consider the two pointsets A and B in the ndimensional real space R n represented by the m \Theta n matrix A and the k \Theta n matrix B respectively. Our principal objective here is to formulate a single linear program with the following properties: (i) If the convex hulls
Parallel Computation of Multivariate Normal Probabilities
"... We present methods for the computation of multivariate normal probabilities on parallel/ distributed systems. After a transformation of the initial integral, an approximation can be obtained using MonteCarlo or quasirandom methods. We propose a metaalgorithm for asynchronous sampling methods and d ..."
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Cited by 217 (9 self)
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distribution function F (a; b) = j\Sigmaj \Gamma 1 2 (2) \Gamma n 2 Z b a e \Gamma 1 2 x \Sigma \Gamma1 x dx: (1) often leads to computationalintensive integration problems. Here \Sigma is an n \Theta n symmetric positive definite covariance matrix; furthermore one of the limits in each
The EdgeBandwidth Of Theta Graphs
 J. Graph Theory
"... . An edgelabeling f of a graph G is an injection from E(G) to the set of integers. The edgebandwidth of G is B 0 (G) = min f fB 0 (f)g, where B 0 (f) is the maximum difference between labels of incident edges of G. The mtheta graph \Theta(l 1 ; : : : ; l mg is the graph consisting of m pa ..."
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Cited by 9 (2 self)
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. An edgelabeling f of a graph G is an injection from E(G) to the set of integers. The edgebandwidth of G is B 0 (G) = min f fB 0 (f)g, where B 0 (f) is the maximum difference between labels of incident edges of G. The mtheta graph \Theta(l 1 ; : : : ; l mg is the graph consisting of m
OPERS AND THETA FUNCTIONS
, 2002
"... Abstract. We construct natural maps (the Klein and Wirtinger maps) from moduli spaces of vector bundles on an algebraic curve X to affine spaces, as quotients of the nonabelian theta linear series. We prove a finiteness result for these maps over generalized Kummer varieties (moduli of torus bundles ..."
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Cited by 3 (1 self)
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derivatives of theta. We interpret the Klein and Wirtinger maps in terms of opers on X. Opers are generalizations of projective structures, and can be considered as differential operators, kernel functions or special bundles with connection. The matrix opers (analogues of opers for matrix differential
Loop Correlators and Theta States in
, 1997
"... Explicit computations of the partition function and correlation functions of Wilson and Polyakov loop operators in thetasectors of two dimensional YangMills theory on the line cylinder and torus are presented. Several observations about the correspondence of two dimensional YangMills theory with ..."
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with unitary matrix quantum mechanics are presented. The incorporation of the thetaangle which characterizes the states of two dimensional adjoint QCD is discussed. 1
A(LDA,*), THETA(*)
"... Note: before using this routine, please read the Users ’ Note for your implementation to check the interpretation of bold italicised terms and other implementationdependent details. 1 Purpose F01RGF reduces the complex m by n (m means of unitary transformations. n) upper trapezoidal matrix A to upp ..."
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Note: before using this routine, please read the Users ’ Note for your implementation to check the interpretation of bold italicised terms and other implementationdependent details. 1 Purpose F01RGF reduces the complex m by n (m means of unitary transformations. n) upper trapezoidal matrix A
Infomax and Maximum Likelihood for Blind Source Separation
, 1997
"... Algorithms for the blind separation of sources can be derived from several different principles. This letter shows that the recently proposed infomax principle is equivalent to maximum likelihood. Introduction. Source separation consists in recovering a set of unobservable signals (sources) from a ..."
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Cited by 170 (2 self)
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set of observed mixtures. In its simplest form, a n \Theta 1 vector x of observations (typically, the output of n sensors) is modeled as x = A ? s (1) where the `mixing matrix' A ? is invertible and the n \Theta 1 vector s = [s 1 ; : : : ; s n ] T has independent components: its probability
A(LDA,*), THETA(*)
"... Note: before using this routine, please read the Users ’ Note for your implementation to check the interpretation of bold italicised terms and other implementationdependent details. 1 Purpose F01RJF finds the RQ factorization of the complex m by n (m n), matrix A, so that A is reduced to upper tria ..."
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Note: before using this routine, please read the Users ’ Note for your implementation to check the interpretation of bold italicised terms and other implementationdependent details. 1 Purpose F01RJF finds the RQ factorization of the complex m by n (m n), matrix A, so that A is reduced to upper
Results 1  10
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770